Computer-implemented method and system  employing compress-sensing model for migrating seismic-over-land cross-spreads

ABSTRACT

A method and a system for implementing the method are disclosed wherein the seismic input data and land acquisition input data may be obtained from a non-flat surface, sometimes mild or foothill topography as well as the shot and receiver lines might not necessarily be straight, and often curve to avoid obstacles on the land surface. In particular, the method and system disclosed, decomposes the cross-spread data into sparse common spread beams, then maps those sparse beams into common-spread depth domain, in order to finally stack them to construct the subsurface depth images. The common spread beam migration and processing have higher signal to noise ratio, as well as faster turn-around processing time, for the cross-spread land acquisition over the common-shot or common offset beam migration/processing. The common spread beam migration method and system disclosed, will eventually help illuminate and interpret the hydro-carbonate targets for the seismic processing.

TECHNICAL FIELD

The present disclosure generally relates to migrating seismic data usingcross-spread land acquisition geometry, in particular using commonspread beam migration for illuminating and interpreting subsurfacehydro-carbonate targets for post seismic processing.

BACKGROUND OF INVENTION 1. Introduction

Exploration seismology aims at revealing the accurate location andamplitude of a target hydro-carbonate within a subsurface, from theprestack seismic data acquired at the earth surface. It usesartificially generated elastic waves to locate mineral deposits(including hydrocarbons, ores, water, geothermal reservoirs, etc.),archaeological sites, and to obtain geological information forpost-processing applying physics and geology concepts, to obtaininformation about the structure and distribution of rock types. Usually,seismic exploration projects are done with a business goal in mind and,hence, cost-benefit is an ever-present concern. Nevertheless, seismicmethods used during exploration alone cannot be used to determine manyof the features that make a project profitable and, even whensupplemented by other data, a unique method for processing theinformation is evident. Seismic exploration usually stops long beforeunambiguous answers are obtained and before all has been learned thatmight possibly be learned, because in the judgment of a person's havingordinary skills in the art, further information is better obtained insome other way, such as by drilling a well. As such, seismic methods arein continual economic competition with other methods. Nevertheless,almost all oil companies rely on seismic interpretation for selectingthe sites for exploratory oil wells. Despite the indirectness of any theexploration methods used, the likelihood of a successful explorationproject is significantly improved if certain known techniques are usedin combination with project specific techniques, especially given theenormous amount information produced by 3-D techniques and computingprocessing power. This is due to the fact, that most receiving systemslike geophones or hydrophones display two-dimensional orthree-dimensional seismic “time sections”, each consisting of largenumbers of seismic traces. Although visual inspection of these seismictime sections can intuitively suggest shapes and locations of subsurfacereflecting formations, the land acquisition input data may be inaccurateor misleading even to a person having ordinary skills in the arttherefore leading to erroneous conclusions as to the actual shapes andlocations of subsurface matters. Accordingly, recorded seismic data isusually manipulated for the purposes of producing migrated sections thatdepict the proper spatial locations of subsurface matter.

2. Recording Seismic and Land Acquisition Input Data

Geophysicists and exploration teams use controlled charges of dynamiteor vibroseis trucks for onshore exploration, while airguns are usedoffshore explorations in order to release waves of sound into the earth.The waves of sound generated by either of these methods, vibrateunderground and bounce back to the surface which instead are received ona receiving device placed in a specific pattern across the terrain.Their number and placement patterns are dependent upon the design, cost,and size of the survey.

As waves of sound vibrate into the receiving system, they are recordedor saved to a memory resource, which are then displayed as soundpatterns or “traces” of subsurface formations. The patterns of thesewave generators and their recording devices create many complexities ofscientific data which tend to be displayed as:

-   -   Two-dimensional data using a single shot-line of data and        representing the intersection of two axes, one horizontal and        one vertical;    -   Three-dimensional surveys which add a horizontal axis, creating        the perception of depth and providing additional data points for        a potentially more accurate geophysical survey.    -   Four-dimensional technology, which is made out of 3D readings of        the same location over time but showing movements of subsurface        hydrocarbons over time.    -   Four-component technology which measures sound waves both        horizontally and vertically.

Regardless of how the acquired data is displayed, it will still beconsidered “raw” or “unprocessed” and, before it can be used it must gothrough a series of computerized processes. These processes—filtering,stacking, migrating and other computer analysis, make the data useablebut require powerful computers to process complex algorithms throughsophisticated computer programs. As computers have become more powerfuland processing techniques more sophisticated, it has become common tore-process seismic data acquired in earlier years, creating newopportunities for exploration that could not originally be derived fromit.

In most situations, the cross-spread land acquisition geometry has denseshots in the shot line direction and dense receiver in the receiver linedirection, especially when the source lines and receiver lines areorthogonal. As such, common shot or common offset processing (includingmigration) are not optimal for these survey data points, and ends uprequiring several more shots or offsets than the number of commonspreads (also known in the art as super shots or super offsets). Forfixed migration parameters, the efficiency of a beam migration for afixed survey is inversely proportional to the number of beam centers oris proportional to the number of traces inside each beam centers.

3. Data Processing

Processing the acquired seismic input and land acquisition data over asurvey region, typically done by geophysicists using special purposecomputers typically comprising hybrid GPU/CPU processors. As such, theprocessing of these techniques are expensive, but they tend to betechnically robust as they provide excellent results. However a closeassociation of the geophysicist, the data, and the processor isabsolutely essential for the results to be useful. It is just that welllogs, known depths, results from ancillary methods, custom formulas,algorithms, as well as the expected results all should be furnished tothe computer system to process the data through a computer-implementedsoftware program. This reduces the originally recorded data from theacquisition step (pre-stack data) into the data volumes (post-stackdata) that are used for interpretation to locate hydrocarbon reservoirsin the subsurface of the earth. There are many steps involved with theprocessing of data, that can be categorized into different classes:

-   -   1) Categorizing acquired data. This step assigns each trace to        its common surface and subsurface location as well as shot to        receiver distance and azimuth for example;    -   2) Time adjustments. This step compensates for travel time        differences due to variations in surface topography and near        surface geologic variations and source to receiver distance        variations. These processes are commonly referred to as        “statics” and “nmo or normal moveout”.    -   3) Wavelet compression. This step collapses reflection events        into a very short duration event instead of the original        recorded signature. This is typically referred to as        “Deconvolution” which has a variety of different technical        implementations.    -   4) Noise Attenuation and signal to noise ratio improvements.        This step removes as much noise as possible while retaining and        enhancing as much of the primary signal as possible.    -   5) Adding (or stacking) traces that have a common subsurface        reflection point.    -   6) Pre and/or post stack imaging. This step relocates all of the        recorded samples and builds an image with the events of the        image displayed at their proper positions in time (or depth) and        space. Imaging, or migration, is one of the most complex and        compute intensive steps in the processing sequence.    -   7) Other steps that can be used to extract more geologic        information from the recorded seismic data to add more        information to the interpretation step.

a. Data Regularization

The problem with any of the acquired data in seismic exploration is itsirregularity therefore impacting its accuracy and relevancy. An issuethat typically affects seismic surveying accuracy is overburdenheterogeneity. This issue refers to a geological area getting overlaidin a model, with a target structure of interest (e.g., hydrocarbonreservoir) in the subsurface. As such, the overburden structure ofinterest will exhibit properties (e.g., velocity and density) that donot vary smoothly in the spatial and/or temporal sense. Instead, theoverburden properties may vary rapidly, such as due to presence of rockfractures or harder and softer regions in the overburden. This oftencauses that exploration seismic data is usually confronted withirregular sampling along the spatial direction, spatial aliasing andlow-resolution wave-equation-based migrations.

Several effective methods have been developed to regularize andinterpolate the irregular seismic data, for example, wave-equation-basedinterpolation (Ronen J., 1987, Wave-equation trace interpolation,Geophys., vol. 52 (pg. 973-984)), prediction error filteringinterpolation (Spitz S., 1991 Seismic trace interpolation in the f-xdomain, Geophys., vol. 56 (pg. 785-794)), Fourier reconstruction (SacchiM D, Ulrych T J., 1996, Estimation of the discrete Fourier transform, alinear inversion approach, Geophys., vol. 61 (pg. 1128-1136)), Seislettransform interpolation (Fomel S, Liu Y., 2010, Seislet transform andseislet frame, Geophys., vol. 75 (pg. V25-V38)), Radon transform (RT)regularization (Zhang Y, Lu W., 2014, 2D and 3D prestack seismic dataregularization using an accelerated sparse time-invariant Radontransform, Geophys., vol. 79 (pg. V165-V177)), and nonlinear shapingregularization for a comprehensive interpolation framework (Chen Y,Zhang L, Mo L., 2015 Seismic data interpolation using nonlinear shapingregularization, J. Seism. Explor., vol. 24). Among those methods,RT-based regularization and interpolation have proved to be effectiveand robust, especially for prestack seismic data. The process ofRT-based seismic data regularization is straightforward and flexible.The irregular spatial sampling data are transformed to the Radon paneland then transformed back to the regular spatial grid to reconstruct theregular seismic data. Nevertheless and no matter what regularizationmethod is used, irregular data manifests differently based upon themodel used:

-   -   When using Kirchhoff prestack migration, data irregularities        often transform into image artifacts.    -   When performing multiple elimination, primary reflections are        contaminated by multiples.    -   Common-azimuth migration might be rendered useless, as the use        of this method implies that the input data is regularly spaced        in midpoint and offset coordinates and therefore relies heavily        on preprocessing by data regularization.    -   4-D seismic monitoring compares 3-D images from data collected        at different production stages in order to monitor changes in        the reservoir. It often requires data regularization onto a        uniform grid for co-locating datasets with different acquisition        geometries.

b. Gaussian Beam Migration

Kirchhoff migration has been the staple of prestack seismic imaging forover a decade. It allows time and depth migration methods to beincorporated within a single basic program, facilitates target-orientedmigration, and enables straightforward migration velocity analysis.While the imaging accuracy of single-arrival Kirchhoff prestack depthmigration has been sufficient for all but the most challengingstructural imaging problems, accuracy comparisons with many wavefieldextrapolation methods have often brought out its shortcomings. Inparticular, in complicated geology, where several arrivals are requiredto give a good image, we must choose one particular arrival, therebydegrading the image.

Recent developments in algorithms and their implementation incomputer-implemented systems, have allowed wavefield extrapolationmethods that can image events to become viable alternatives to Kirchhofftechniques. However, these methods can have problems imaging steep oroverturned events, as well as accounting for anisotropy. Furthermore,although wavefield extrapolation methods can be affordable, they arestill much more expensive than Kirchhoff migration. As a result, theyare typically reserved for situations that really require multi-arrivalimaging, such as geology below complicated salt bodies. One of saidmethods has been Gaussian Beam Migration which has been widely appliedto the migration imaging (See Hill, N. R., 1990, Gaussian beammigration; Geophysics, v. 55, 1416-1428; and Hill, N. R., 2001, PrestackGaussian-beam depth migration, Geophysics, v. 66, 1240-1250).

The Gaussian beam migration method has advantages over the Kirchhoffmigration method, in areas of steep dip imaging, as well as caustics andmulti-valued travel time. This method has then been exploited byscientific researchers, which ended up extending studies on the Gaussianbeam migration, to anisotropic Gaussian beam migration, true-amplitudeGaussian beam migration, Gaussian beam reverse time migration,dynamically focused Gaussian beam migration and sparse Gaussian beammigration. Regardless of the method used, true Gaussian beam migrationincludes basically four steps: decomposing seismic data; representingthe seismic data with Gaussian beams; propagating the Gaussian beamsdownward; and superposing, according to an imaging condition,contributions of the Gaussian beams at an imaging point. The decomposingthe seismic data and representing the seismic data with Gaussian beamsis the key factor for the Gaussian beam migration, which decides thecomputation amount and imaging results of the migration.

In the existing sparse Gaussian beam migration imaging method, sparsedecomposition is applied to the seismic data by using Gaussian beamswith a curvature of zero. But the seismic data has a curvature, andtherefore, both a width of a Gaussian beam-based function and a spacingbetween centers of two adjacent Gaussian beams should be small enough toenable appropriate fitting of the seismic data. However, in the existingGaussian beam migration imaging methods, a large number of waveformfunctions are obtained through decomposition, and it is required toperform migration imaging on each of the waveform functions during themigration of seismic imaging, and thus the computational efficiency ofthe entire migration is low.

In summary, the Gaussian beam migration imaging method retains thestrengths of Kirchhoff migration, but can also image multiple arrivals.Although Hill, N. R., supra, gives the theoretical basis for Gaussianbeam migration in his two classic papers, the method involves many stepsand is difficult to implement. Indeed this is, perhaps, the main reasonit has not become more popular. Gaussian Beam migration solves many ofthe imaging accuracy problems of single-arrival Kirchhoff migration,while retaining many of the advantages of the Kirchhoff method includingits ability to image steeply dipping or overturned events, as well asimaging in the presence of TTI anisotropy.

Nevertheless, other beam migration methods have been used in the art. Inparticular, common offset beam migration (Rose Hill, 1996, 2001) andcommon shot beam migration (Samuel Gray, 2005) which are well known andwidely used in the seismic production. Even with the obvious advantagesthese two other methods provide, few or none applications of commonspread beam processing in the cross-spread acquisition, are mentionedfor the seismic processing. In theory, the common spread beam migrationsignificantly improves the signal to noise ratio and turn-aroundefficiency for the land depth processing. As cross-spread surveys inland acquisition proliferate, the demand for common-spread beammigration velocity model building and imaging also increases.

c. Ray Tracing

The concept of a ray tracing is very useful. It basically boils down todrawing a line in space that corresponds to the direction of the flow ofradiant energy. Rays are a geometric idealization because they have nowidth. As such, a ray is a mathematical device rather than a physicalentity. In practice, a person having ordinary skills in the art canproduce very narrow beams (as, for example, a laser beam), and thereforea ray may then become the unattainable limit on the narrowness of such abeam. Similar to the lines of geometry, rays are a convenient fiction.They exist in the real world as a beam of light; and beams have width.In the same way, we may think of seismic rays as idealized beams in thedirection of the flow of seismic energy.

The procedure for executing a ray equation is similar in geophysics asit is in optics. It basically comprises of solving wave equations thatdescribe compressional waves traveling at the P-wave velocity of α;shear waves which travel at the S-wave velocity of β; harmonic waves offrequency ω with constant amplitudes and zero initial phases. Theformula can then be written for P-waves as:

φ(x,t)=φ₀(x)e ^(−iωt)  (1)

The concept of a ray describes the path of a wave packet through theisotropic medium, being at all times perpendicular to the wavefront inthe direction of the increasing phase. Like the travel time T(x), whichis defined as a time taken for wavefront to travel from a referencepoint x₀ to the arbitrary point x, the eikonal is defined relative tothe phase at the reference point S(x)=α₀T(x) and the eikonal equationcan be re-written in terms of the travel time and the wave speed v(x):

$\begin{matrix}{\left( {\nabla S} \right)^{2} = {\left. {\frac{\alpha_{0}^{2}}{\alpha^{2}}2}\Rightarrow\left( {\nabla T} \right)^{2} \right. = \frac{1}{{v(x)}^{2}}}} & (2)\end{matrix}$

The ray path may then be described by the function x(s) where s is thecurvilinear distance from the reference point along the ray path.Furthermore, the slowness vector p can then be defined as p(s)=∇T,because its magnitude at a point x(s) is equal to the reciprocal of thevelocity at that point. Applying the condition that the ray path isorthogonal to the wavefronts yields:

$\begin{matrix}{\frac{dx}{ds} = {{v\;{\nabla T}} = {vp}}} & (3)\end{matrix}$

By differentiating the eikonal equation (2) with respect to s andcombining the result with (3) the following (4) ray equation isobtained, and the travel times may then be calculated.

$\begin{matrix}{{\frac{d}{ds}\left( {\frac{1}{v},\frac{dx}{ds}} \right)} = {\nabla\left( \frac{1}{v} \right)}} & (4)\end{matrix}$

Other ray tracing modeling programs exist as well, that are used bypersons skilled in the art to obtain 2D or 3D models without aninversion stage. One of said programs is called RAYAMP (See Spence, G.D., 1983. RAYAMP: An algorithm for tracing rays and calculatingamplitudes in laterally varying media: program documentation; Univ. ofBritish Columbia), but requires that the user defines the velocitystructure within a 2D model, with two types of boundaries: model anddivider boundaries. A model boundary is a straight line of an arbitrarydip. It has assigned a constant velocity along its length and a nonzerovelocity gradient normal to its length. A divider boundary is assigned avelocity zero and it separates two regions with different velocity andvelocity gradient. Blocks may thus be defined, in which the velocity,magnitude and direction of velocity gradient are arbitrary. The ray pathwithin a given block is considered by the program as a circular arc(because of constant velocity gradient), for which the travel time andthe distance traveled may be calculated using very simple analyticalexpressions.

Another ray tracing program used by individuals skilled in the art iscalled JIVE3D (See James W D. Hobro; “Jive3D”;https://bullard.esc.cam.ac.uk; 2006). Modelling with JIVE3D is dividedinto forward and inversion modelling. At each iteration of thealgorithm, a set of synthetic travel-time data is produced, from aworking velocity model, and the Fréchet derivatives which link smallchanges in model parameters to small changes in travel-time data, arecalculated. These synthetic data and Fréchet derivatives are then passedto the inversion stage, which compares the synthetic data with the givenreal data and calculates a new model based on a set of linearapproximations until the model converges to a point that optimizes thespecified norm for smoothness and best fit.

As such, given the current state of ray-tracing, not one solution can beused. In particular, RAYAMP can only used for 2D forward modelling,which means, that it cannot produce inversions to the starting model. Ifmore realistic models are to be obtained with RAYAMP, a person havingordinary skills in the art would have to include some sort of algorithmfor automatic search of the best set of parameters, which are definingthe model, such as; different model boundaries, velocity field andsmoothing criteria. The procedure of JIVE3D is more practical, as itincludes inversion and is therefore able to construct the final model asan evolution of the starting model.

d. Summary

When imaging reservoirs beneath salt bodies or along steep flanks,conventional single-arrival ray-based technology (e.g. Kirchhoffmigration) encounters serious problems as the imaging process is notable to reconstruct the scattered energy of the highly irregular(rugose) top salt, and information from waves that pass through the topof a salt mass is effectively lost (or only partially imaged). Beammigration (Hill, 1990, 2001) images the multi arrivals naturally, butalso greatly reduces costs of following migrations for tomographyiterations, if the dense data volume is decomposed to seismic elementsand saved for future iterations. A laser beam migration approach limitsthe beam spread to a “laser-thin” region (Xiao et al., 2014), and canaccommodate large lateral velocity variations to the accuracy of thecentral rays, while imposing no dip limitations on images.

Therefore, aiming at some defects of existing technology, the presentembodiments of this invention introduces a new computer-implementedmethod and system employing compress-sensing model for migrating seismicover land-cross spreads.

SUMMARY OF THE INVENTION

The present invention discloses a novel method for locating a subsurfacereflectors through common spread processing, specifically designed forcross-spread geometry. This method will have less numbers of commonspreads than number of common shots or common offsets on a standardcross-spread land acquisition geometry; as it has been observed in theart that more traces inside each beam center lead to higher signal tonoise ratio for the decomposition and less turn-around time for themigration. As such, a proposed embodiment of the present inventioncomprises common-spread beam migration as a superior method tocommon-shot/offset beam migration for a land cross-spread acquisitiongeometry, in terms of efficiency and quality.

The technique of embodiments of the invention, present the novelgeophysics concept of compressed-sensing related to “downwardcontinuations” as described in the art, but also referred to as an“extrapolation”, which implies that there are not any directionalconstraints from the projection. Its objective is to simplify thetime-consuming seismic processing done in the beam domain. Moreparticularly, with compress-sensing, beam technology can decompose thedense data into sparse seismic elements and save for future seismicprocessing. The sparse beam elements are described by most importantattributes including location, dips and wavelets, and capable ofrepresenting those complex/dense prestack dataset for followingtomography and migration.

Therefore, objects of embodiments of the present invention, involvedetermining an efficient, accurate common spread beam migration methodfor land cross-spread acquisition geometry. In particular, objects ofthe present invention perform beam decomposing methods, while storingthe sparse beam elements to a memory resource (e.g. disk), in order toreduce the computation time required for following seismicmigration/stacking.

Further details, examples and aspects of the invention will be describedbelow referring to the drawings listed in the following.

BRIEF DESCRIPTION OF THE DRAWINGS

The teachings of the present invention can be readily understood byconsidering the following description in conjunction with theaccompanying drawings.

FIG. 1, is a schematic diagram showing top view of a survey regiondepicting a cross-spread acquisition geometry with receiver and shotlines, according to an embodiment of the present disclosure;

FIG. 2, is a flow chart showing the computer-implemented method formigrating seismic-over-land cross-spreads, according to an embodiment ofthe present disclosure;

FIG. 3, is an electric diagram, in block form of the system apparatusprogrammed to perform the computer-implemented method for migratingseismic-over-land cross-spreads, according to an embodiment of thepresent disclosure;

FIG. 4, illustrates a flow chart of the sub-routine of decomposing theretrieved seismic model input data into sparse common-spread beams forirregularly generated common-spread beam centers as executed by thenon-transitory program computer readable memory storage device; and

FIG. 5, illustrates a flow chart of the sub-routine of decomposing theretrieved seismic model input data into sparse common-spread beams forregularly generated common-spread beam centers as executed by thenon-transitory program computer readable memory storage device; and

DETAILED DESCRIPTION OF THE INVENTION

Reference will now be made in detail, to several embodiments of thepresent disclosures, examples of which, are illustrated in theaccompanying figures. It is noted that wherever practicable similar orlike reference symbols may be used in the figures and may indicatesimilar or like functionality. The figures depict embodiments of thepresent disclosure, for purposes of illustration only. One skilled inthe art will readily recognize from the following description thatalternative embodiments of the structures, systems, and methodsillustrated therein may be employed without departing from theprinciples of the disclosure described herein.

Because land data acquisition may be performed on non-flat surfaces,sometimes mild or foothill topography, the shot and receiver lines aretherefore not necessary straight, and often posed curved, in order toavoid obstacles on the land surface. As such, these conditions bringdifficulties to the regularization stage prior to common-spread beammigration. Fortunately, with the proposed computer-implemented methodand system, it is no longer needed to relate to a flat surface, butrather to relate to a smooth floating horizon. As long as, in a beamcenter, the topography is flat or smooth enough and the shot andreceiver lines are straight or orthogonal enough, the impact of thetopography and line weathering will be minimal to be ignored, afterproper preprocessing are taking into account.

During land cross-spread acquisition geometry, it is typical to havedense shots, in both the shot line direction as well as in the receiverline direction, where the source lines and receiver lines areorthogonal. A common spread, X_(c), from a cross-spread acquisition isdefined as all the traces with the source at one shot line and receiversat one receiver line. After converting to local survey coordinates,within the present computer-implemented method one can have shot linecoordinates s_(x) as constant for each shot line, and receiver linesg_(y) as constant for each receiver line, and the survey noted as XSG,and a common spread function identified as X_(c)(s_(x),g_(y)).Coordinates s_(y) and g_(x) are also constant for each shot line orreceiver line respectively; while source slowness is then identified asp_(y) ^(s), while receiver slowness as p_(x) ^(r). In a super commonspread, the source and receiver coordinates are inside a range in thesource line or receiver line direction, instead of only a constant. Thisis useful for land acquisition with topography, and to further improvethe efficiency and increase the signal to noise ratio. A common-spreadbeam migration is then defined as the process of first sorting theacquired data in common-spread domain, and then performing beammigration with corresponding slant stacking and imaging criteria, on onecommon spread input gather by one common spread.

Once the acquired data has been regularized, a process wherein data lostduring regularization is recovered, gets initiated. Although multiplemethods to achieve this exist, Compressed Sensing (CS) presents itselfas a novel sensing/sampling paradigm that allows the recovery of sparse(few non-zeros) or compressible (quickly decaying entries) signals fromfar fewer measurements than the Nyquist rate. The sparsity assumption iseasily realized in practice, as, for instance, natural images are sparsein the Wavelet domain (e.g. JPEG2000 compression) and seismic images arewell represented in terms of curvelets. (See Candes, E., Romberg, J.,and Tao, T., 2006, Robust uncertainty principles: exact signalreconstruction from highly incomplete frequency information: InformationTheory, IEEE Transactions, 52, 489-509) and (Donoho, D., 2006,Compressed sensing: Information Theory, IEEE Transactions on, 52,1289-1306) first provided rigorous theory underlining under whichconditions a sparse signal could be recovered from subsampledmeasurements.

According to the compressed-sensing theory employed by the presentcomputer-implemented method and system, successful dimensionalityreduction hinges on an incoherent sampling strategy where coherentaliases are turned into relatively harmless white Gaussian noise. Thechallenges of adapting this approach to real-life problems inexploration seismology are threefold (See Herrmann, F. J., and Li, X.,2012, Efficient least-squares imaging with sparsity promotion andcompressive sensing, Geophysical Prospecting, vol. 60, pp. 696-712).First, seismic data acquisition is subject to physical constraints onthe placement, type, and number of (possibly simultaneous) sources, andnumbers of receivers. These constraints in conjunction with theextremely large size of seismic data calls for approaches specific tothe seismic case. Second, while CS offers significant opportunities fordimensionality reduction, there remain still challenges in adapting thescientific-computing workflow to this new approach, and again, CS offersan opportunity to make computation much more efficient. Third, seismicwavefields are highly multiscale, multidirectional, and are the solutionof the wave equation. This calls for the use of directional andanisotropic transforms, e.g., curvelets

Nevertheless, the present computer-implemented method and system usescompressed sensing algorithms for beam technology to decompose the densedata into sparse seismic elements and be saved to a memory resource, forfuture seismic processing. The saved sparse beam elements are describedby their most important attributes including location, dips andwavelets, and capable of representing those complex/dense prestackdataset for post-processing tomography and migration. By simplifying theseismic processing in the beam domain, the time-consuming seismicpost-processing (e.g. migration, stacking) can be greatly reduced toacceptable turnaround time.

Common offset beam migration and common shot beam migration are wellknown and widely used in the seismic production. Even with the obviousadvantages, few or none applications of common spread beam processing inthe cross-spread acquisition, are mentioned for the seismic processing.Thus the common spread beam migration method and system of the presentinvention processes the workflow by (1) sorting the acquiredcross-spread data in to common-spread domain input gather; (2) readingin topography; (3) regularizing to flat surface or on topography; (4)decomposing to sparse beams; (5) raytracing; and (6) stacking. Inparticular, the computer-implemented method and system uses a dedicatedmigration kernel programmed in the non-transitory program computerdevice for stacking, in the following algorithm form:

For each common spread XSG{  For each beam center location{   Taperedlocal slant stacks   For all image points in the aperture{    Sum theslant-stacked input trace into the image using multi-   arrivaltime-table.   }End loop over image points  }End loop over beam centerlocation }End loop over common spread

Nevertheless, a person having ordinary skills in the art, would soonrealize that if the non-transitory program computer readable devicewants to do more scanning on the input, the migration kernel algorithmis then executed in the alternative form of:

For each common spread XSG{  For each beam center location{   Taperedlocal slant stacks   For each midpoint ray parameter{     For eachcoarse-grid image point{     Scan over offset ray parameters for theminimum value of  imaginary time     }End loop over coarse grid of imagepoints     For all image points in the aperture{      Sum theslant-stacked input trace into the image using     multi-arrivaltime-table.     }End loop over image points    }End loop over midpointray parameters  }End loop over beam center location }End loop overcommon spread

FIG. 1 illustrates a seismic survey region, 101, in which the preferredembodiment of the present invention is useful. It is important to note,that the survey region of FIG. 1 is a land-based region represented as102 and that a complete survey plan, including swaths of shot (104) andreceiver locations (105), as shown in FIG. 1 may vary depending uponsurvey characteristics like goals, budget, resource, and time.

Persons of ordinary skill in the art, will recognize that seismic surveyregions like 101 produce detailed images of local geology in order todetermine the location and size of possible hydrocarbon (oil and gas)reservoirs, and therefore a potential well location 103. Landacquisition geometry represented by FIG. 1 commonly is carried out byswath shooting in which receiver cables are laid out in parallel lines(inline direction) and shots are positioned in a perpendicular direction(crossline direction). In these survey regions, sound waves bounce offunderground rock formations during blasts at various points of incidenceor shots 104, and the waves that reflect back to the surface arecaptured by seismic data recording sensors, 105, transmitted by datatransmission systems, 305, wirelessly, 303, from said sensors, 105, thenstored for later processing, and analysis by the digital highperformance computing system of FIG. 3. Although shots 104, arerepresented in FIG. 1 as a cross-spread pattern geometry with shotlines, 106 mostly running horizontally, a person having ordinary skillsin the art, would soon realize that said pattern could easily berepresented in other ways, such as vertically, diagonally or acombination of the three. Similarly, the recording sensors 105, areplaced on receiver lines, 107 shown running across the shot lines 106but could've also been represented. The swath shooting method yields awide range of source-receiver azimuths, which can be a concern duringanalysis by system computer 301. The source-receiver azimuth is theangle between a reference line, such as a receiver line or a dip line,and the line that passes through the source and receiver stations.Nevertheless, because of operating conditions, uniform coverage as shownin FIG. 1, usually is not achievable over the entire survey area

With regards to FIG. 2, it illustrates a flow chart 201 with an overviewof the preferred embodiment of the invention. The system acquiring phase202 initiates the process by retrieving seismic model input data, 203,as well as land acquisition input data, 204, from the survey region 101.In particular, four different types of inputs are retrieved from as partof the land acquisition data of the survey region: upscaled well logdata represented in time domain, a set of angle image gathers, horizonsinformation, and seismic velocity data. Another set of data gathered bythe present invention is seismic velocity, 305. The acquired seismicvelocity (both compressional and shear) is a fundamental inputrequirement of the proposed embodiment, as it comprises of materialproperties that vary with changes in conditions both external (stress,temperature) and internal (fluid saturation, crack density). As such,monitoring of changes in these external or internal conditions is a goalof geophysical investigations such as the one performed by the presidentembodiment as it helps with earthquake prediction (via stress changemonitoring) and reservoir exploitation (via fluid saturationmonitoring).

The seismic model input data may be obtained directly from the welllocation and remotely transferred onto a database, 304, for furtherprocessing, such as that contemplated on FIG. 4. It may also beretrieved from database 304, by the non-transitory program computerreadable device 306. As such, the seismic model input data may compriseof P-wave velocity, S-wave velocity, density, a set of angle imagegathers consistent with the amount of points of incidence 104 within asurvey region 101 represented in the time or depth domain at the variousangles of incidence. Nonetheless, since this seismic survey data 203 and204 data is too raw, noisy, or from various points of incidence 104, itneeds to be further processed. This further refinement occurs after thecomputer-implemented method stores to the memory resource, at 205, bothof sets of acquired input data.

The non-transitory computer program device 306 then receives a signalfrom the memory resources 304, indicating that the acquired data 203 and204 have been stored and initiates a set of parallel operations whichcomprise of the sub-routine of decomposing the seismic model input datainto spare common-spread beam centers, 207, as well as regularizing theland acquisition input data, 208. At which point, the system computer301, then sends a message hook to the non-transitory program computerreadable device, 306, to load both the decomposed seismic model inputdata from 207, as well as the regularized land acquisition input data,208; to initiate routine 209 of generating common-spread gather. Thesegenerated common-spread gathers are then stored at 212 to the memoryresource, 304. A common-spread gather refers to a group of prestacktraces with a limited range of spread locations or spread locationcoordinates in between them. These traces, after decomposed, can containsome irregularities, that need to be addressed differently by thesub-routine. Non-transitory computer program device 306, will messagethe computer system device to indicate to the person having ordinaryskills in the arts operating it, to make the determination as to whetherthe decomposed common-spread beam centers comprise of regular orirregular traces. The term “common spread gather” is often used for suchtrace gathers, even though the spread locations or spread coordinateswithin a gather do not have to be the same. A common-spread gather canbe considered “valid” if it has a continuous coverage of the size of thewhole survey, 2D or 3D. The word “continuous” is used in the sense thatthe pertinent data are sampled with the finest spatial rate of thesurvey. The word “valid” is used in the sense that such a gather, byitself, provides a continuous full-range subsurface image. This groupingof seismic data into common-spread gathers is a routine procedure forvelocity analysis, prestack imaging and prestack data interpretation,such as AVO analysis.

The system computer 301, then sends a message hook to the non-transitoryprogram computer readable memory device, 306, to initiate filteringroutine 210 which filters the wave-bands as well as theaccuracy/frequencies of the common-spread gathers, using well knownalgorithms of the art such as band pass, or low-pass filtering. Thisthen triggers the non-transitory program computer readable memorydevice, 306, to message the computer system device 307 to display onmonitor 309 whether the filtering was performed acceptably to a personhaving ordinary skills in the art. If the user or person of ordinaryskills in the arts is not satisfied with the filtering 210, it theninputs a rejection command through the use of keyboard 310, and mouse311 so that the computer system 307 can communicate with thenon-transitory program computer readable memory device, 306 toperforming filtering 210, using a different set of pre-programmedalgorithm. This loop continues until the user or person having ordinaryskills in the art inputs either by the use of keyboard 310 or mouse 311,that filtering 210 was acceptably performed. Upon confirmation of anacceptable filtering, the non-transitory program computer readablememory device, 306, generates a set of filtered common-spread gathers211 that are then stored at 212, to the memory resource 304.

The system computer 301, then sends a message hook to the non-transitoryprogram computer readable memory device, 306, to initiate the retrievalof the filtered common-spread beam centers at 213. Once retrieved, thenon-transitory program computer readable memory device, 306 beginscomputing laser-beam raytracing 214 using velocity distribution,shooting or bending algorithms to trace the ray path from a point ofincidence 104, to a receiver location 105. A ray tracing algorithmprogrammed in the non-transitory program computer readable memorydevice, 306 is used for forward modelling and applies seismic traveltimeinversion with the purpose of determining the velocity model andinterface structure. This algorithm is done for each ray in isotropicand vertical transversely isotropic (VTI) media. The main advantage ofthis algorithm (See also Zelt C. A. Smith R. B., 1992. Seismictraveltime inversion for 2-D crustal velocity structure; Geophysics; J.Int., vol. 108, pp. 16-34) is its flexibility in model parametrizationand velocity determination. To obtain velocity model and interfacestructure, the programmed algorithm applies seismicrefraction/wide-angle reflection traveltime calculations. Thenon-transitory program computer readable memory device, 306 generates at215 the laser-beam traced rays, which are then stored to memory resource304 at step 216. Once stored, the memory resource sends a message hookto the non-transitory program computer readable memory device 306, toretrieve at step 217 the common-spread gathers stored at 212 to thememory resource 304 as well as the traced laser-beam rays stored at 216.The non-transitory program computer readable memory device, 306 thencomputes common spread slant stacking at step 218, by applying linearmoveout and summing amplitudes over the offset axis. An underlyingassumption of the slant stacking step 218, is that of a horizontallylayered earth model. Conventional processing is done primarily inmidpoint-offset coordinates. Slant stacking replaces the offset axiswith the ray parameter p axis, which is the inverse of the horizontalphase velocity, and non-transitory program computer readable memorydevice, 306 at step 219, begins computing the common-spread migrationalgorithm to generate common spread beam migration image. The imagegenerated at 219, gets then store at step 220 into the memory resource304, and the image displayed by the computer system's 307, monitor 309.The user may then print the migrated image generated at 219, or sharethe files with other computer-implemented programs for furtherprocessing or analysis.

As it pertains to FIG. 3, it illustrates a functional block diagram of acomputer system apparatus, 301, used to perform an array of operationsof the computer-implemented method 201 used for subsurface caverecognition in a survey region. The computer system apparatus, 301,further incorporates (wired and/or wirelessly) memory resources, 304,for storing data transmitted from the receiving sensors 105, usingwireless transmission systems, 305, and transmitted wireless, 303, anon-transitory program computer readable memory device storage, 306, anda computer system device, 307.

The computer system device, 307, acts as a user interface thenon-transitory program computer readable memory storage device, 306; toinput, set, select, and perform the operations of retrieving, computing,generating, invoking, determining, converting, and correcting functions(the message hook procedures). Said computer system device, 307, isconnected to (wired and/or wirelessly) to the non-transitory programcomputer readable memory storage device 306. The computer system device,307, further includes other devices like a central processing unit(CPU), 308, a display or monitor, 309, a keyboard, 310, a mouse, 311,and a printer, 312.

The system computer device, 301, has firmware, a kernel and a softwareproviding for the connection and interoperability of the multipleconnected devices, like the memory resources for storing data, 304, thetelemetry system 305, the non-transitory program computer readablememory device, 306, and the computer system device, 307. The systemcomputer, 301, includes an operating system, a set of message hookprocedures, and a system application.

Furthermore, because performance is the always important issue, thesystem computer device, 301, uses the non-transitory program computerreadable memory device, 306 to ensure that the beam migration steps willnot be bottlenecked by the system computer device 301 I/O, or anynetwork communications. In fact, Apache Hadoop distributed file-systemand proper data-compressions, as well as smart file caching according tothe data will ensure that the computer-implemented method is onlylimited by the memory/cache speed and CPU computing power, and nothingelse.

The operating system embedded within the system computer 301, may be aMicrosoft “WINDOWS” operating system, OS/2 from IBM Corporation, UNIX,LINUX, Sun Microsystems, or Apple operating systems, as well as myriadembedded application operating systems, such as are available from WindRiver, Inc.

The message hook procedures of system computer 301 may, for example,represent an operation or command of the memory resources, 304, thecomputer system device, 307, the non-transitory program computerreadable memory storage device, 306, which may be currently executing acertain step process or subroutine from the computer-implemented methodfor small cave recognition using seismic reflection data.

The set of message hook procedures may be first initiated by an inputfrom: the user, like the entering of user-defined values or parameters;the manipulation of the computer system device, 307; the processing ofoperations in the non-transitory program computer readable memory devicestorage, 306; or automatically once certain data has been stored orretrieved by either the memory resources, 304, or the non-transitoryprogram computer readable memory device storage, 306. Based on any ofthese inputs, processes or manipulation events, the memory resources,304, the non-transitory program computer readable memory storage device,306, or the computer system device, 307; generate a data packet that ispassed to the system computer, 301, which are indicative of the eventthat has occurred as well as the event that needs to occur. When systemcomputer, 301, receives the data packet, it converts it into a messagebased on the event, and executes the required step of thecomputer-implement method. The computer-implement method includes a setof message hook lists that identifies the series of message hookprocedures. When the operating system receives the message, it examinesthe message hook list to determine if any message hook procedures haveregistered themselves with the operating system. If at least one messagehook procedure has registered itself with the operating system, theoperating system passes the message to the registered message hookprocedure that appears first on the list. The called message hookexecutes and returns a value to the system computer, 301, that instructsthe system computer, 301, to pass the message to the next registeredmessage hook, and either 304, 306 or 307. The system computer, 301,continues executing the operations until all registered message hookshave passed, which indicates the completion of the method by theidentification of magnetic inference 313.

According the preferred embodiment of the present invention, certainhardware, and software descriptions were detailed, merely as exampleembodiments and are not to limit the structure of implementation of thedisclosed embodiments. For example, although many internal, and externalcomponents of the receiving system apparatus of FIG. 3 have beendescribed, those with ordinary skills in the art will appreciate thatsuch components and their interconnection are well known. Additionally,certain aspects of the disclosed invention may be embodied in softwarethat is executed using one or more, receiving systems, computers systemsdevices, or non-transitory computer readable memory devices. Programaspects of the technology may be thought of as “products” or “articlesof manufacture” typically in the form of executable code and/orassociated data that is carried on, or embodied in, a type of machinereadable medium. Tangible non-transitory “storage” type media anddevices include any or all memory or other storage for the computers,process or the like, or associated modules thereof such as varioussemiconductor memories, tape drives, disk drives, optical or magneticdisks, and the like which may provide storage at any time for thesoftware programming.

As it pertains to FIG. 4, it illustrates a flow chart of the sub-routine207 of decomposing the retrieved seismic model input data into sparsecommon-spread beams for irregularly generated common-spread beam centersas executed by the non-transitory program computer readable memorystorage device, 306 with its output being the generation common-spreadgather 209. The lack of regularity in beam centers, is mostly given bythe acquisition of data through irregular magnetic micro-pulsations, aswell as irregular topography. Thus, a person of ordinary skills in theart will recognize if the decomposed beam centers being generated areregularly or irregularly. Whatever the formation of the beam centersappears to be through the processing of the present embodiments, aperson of ordinary skills in the art operating the system computer 301,will have to indicate to the non-transitory computer program device 306,through the use of computer system 307 whether the computer-implementedmethod continues the computation through the use regular or irregularcommon-spread beam forming. For regular beam forming during decomposingprocess 207, the sub-routine gets initiated by the non-transitorycomputer program device 306, after receiving command from the computersystem device 307. The non-transitory computer program device 306,signals the memory resource 304, that it will begin retrieving atsub-step 402 the land acquisition input data 204. Once thenon-transitory computer program device 306 has said data it can beginacquiring local data gathers at step 403. Nonetheless, because the datais too raw to be processed, the local data gathers need to be filteredat step 404, using well-recognized algorithm in the art like low-pass,band-pass, waveforming, and others. Because seismic waves penetrate deepinto the Earth providing key information to help detect geometricstructures and physical characteristics of the Earth's interior, theunderstanding of high-resolution spatial complexities and deep Earthstructures is limited by the number of seismic stations and the qualityof the data they recorded. One fundamental solution is to increase thedensity of evenly distributed seismic stations (either permanent ortemporary, on land or ocean bottom). But increasing station density isproblematic because of the high costs of instrument deployment andmaintenance. So instead, persons skilled in the art have implemented anarray of clipping procedures to use data deployed seismic stations andretrieved by sensors 105. A clipped waveform although a basic processing(e.g. bandpass filtering, and removing instrument response) it is stillproblematic because of the aliasing of the frequency components (alsocalled frequency leakage), as it involves convolution or deconvolutionthat are basically frequency-based operations. To perform these basicprocessing procedures, it may be an alternative to select only theunclipped portions of the waveform, but this would require applying adamping taper function on the unclipped portions of the waveform thatare close to the clipped portions, which causes additional waste ofseismic data (usually 30 to 50 samples). The seismic records close tothe epicenter convey much more valuable information of the earthquakesource and the regional structures than data from stations further away.By definition, typically waveform amplitudes are larger closer to theshot epicenter 104 and therefore prone to be clipped. With theincreasing density of seismic stations (especially for temporary seismicarray), the computer-implemented method anticipates that the totalnumber of clipped waveforms is only going to increase and therefore,performs the imperative step 405 of productively clipping data usingmax-clipping, min-clipping, taper-width, taper-shape, for laterprocessing the zero-phase reshape wavelet. The seismic wavelet used bysub-routine 207, are the link between seismic data (traces) on whichinterpretations are based and the geology (reflection coefficients) thatis being interpreted, and it must be known to interpret the geologycorrectly. However, it is typically unknown, and assumed to be bothbroad band and zero phase. Providing this broad band, zero phase waveletis the processing goal of deconvolution. Unfortunately, this goal israrely met and the typical wavelet that remains in fully processed landacquisition input data is mixed-phase. Differences in mixed-phasewavelets result in mis-ties and often incorrect interpretations.Therefore, sub-routine 207 assumes that the land acquisition input datacontains a broad band—zero phase wavelet that is nearly always wrong,and performs the step of wavelet reshaping at 406 which results onmixed-phase wavelets remaining in fully processed seismic data.Beamforming is then performed at step 407 in order to filter spacetimesignals from the processed wavelets. Step 407 is designed to isolatesignals travelling in a particular direction, and use weighted delay andsum beamformer. The output of step 407, is the average of the linearcombination of delayed signals. In other words, the beamformer output isformed by averaging weighted and delayed versions of receiver signals.The delay is chosen such that the passband of beamformer is directed toa specific direction in the space and then used to compute semblance atstep 408. The step of computing semblance is used in the refinement ofland acquisition input data. The use of this technique along makes itpossible to greatly increase the resolution of the data despite thepresence of background noise. Persons having ordinary skills in the artwill soon recognize that the new data received following the semblanceanalysis is usually easier to interpret when trying to deduce theunderground structure of an area. Weighted semblance can also be used bythe non-transitory program computer readable memory storage device, 306,upon selection from the person having ordinary skills using the computersystem device 307 for increasing the resolution of traditional semblanceor make traditional semblance capable of analyzing more complicatedseismic data. In the present embodiment, the computation of semblanceutilizes the following algorithm:

$\begin{matrix}{{{D_{X_{c}}\left( {X,p^{\prime},\omega} \right)} = {{\frac{\omega}{\omega_{x}}}{\int{\int{\frac{{dx}^{\prime}{dy}^{\prime}}{4\pi^{2}}{D_{X_{c}}\left( {r^{\prime},\omega} \right)}e^{\lbrack{{i\;\omega\;{p^{\prime} \cdot {({r^{\prime} - X})}}} - {{\frac{\omega}{\omega_{x}}}\frac{{{r^{\prime} - X}}^{2}}{2\omega_{x}^{2}}}}\rbrack}}}}}};} & (5)\end{matrix}$

Once the land acquisition input data 204 has been filtered, thenon-transitory program computer readable memory storage device, 306signals the computer system device 307, to display on monitor 309 theshot and receiver events, as well as each wavelet. The person havingordinary skills in the art, operating the computer system device 307,will soon realize from observing the display monitor 309, which eventsand wavelets are relevant from each semblance, and perform the step 409of selecting them by using a combination of keyboard 310 and mouse 311from the computer system device 307. Upon selection, the person ofordinary skills operating the computer system device 307, will bepresented by a graphical user interface in monitor 309 asking to confirmselection. If selection is confirmed, then the computer system device307 messages the non-transitory program computer readable memory storagedevice, 306 to store at 410, the selected event(s) and wavelet(s) foreach semblance onto the memory resource 304. If the selection is notconfirmed, the non-transitory program computer readable memory storagedevice, 306 presents the events and wavelets through the computersystem's 307 monitor 309 again for selection. Once the selected event(s)and wavelet(s) is/are stored at 410, the system exits sub-routine andfinalizes the generation of common-spread gather 209.

Similarly, FIG. 5, illustrates a flow chart of the sub-routine 207 ofdecomposing the retrieved seismic model input data into sparsecommon-spread beams for regularly generated common-spread beam centersas executed by the non-transitory program computer readable memorystorage device, 306 with its output being the generation common-spreadgather 209. In the case of regularly generated common-spread beamcenters, the process uses commonly known in the art method mainlycomprising of Fast Fourier Transformations. Under the alternative orregularly formed beams, the non-transitory computer program device 306,signals the memory resource 304, that it will begin retrieving atsub-step 502 the land acquisition input data 204. Once thenon-transitory computer program device 306 has said data it can beginacquiring local data gathers at step 503. Once the local data gathers503 have been acquired from the land acquisition input data 204, thenon-transitory program computer readable memory storage device, 306,computes a Fast Fourier Transform analysis at 504, by extracting theseries of sines and cosines and transforming from a function of timeinto a function of frequency. Furthermore, the Fast Fourier Transform(FFT) breaks up a transform of length N into two transforms of lengthN/2 using the identity sometimes called the Danielson-Lanczos lemmathereby reducing the computation resources needed for N points. Thenon-transitory computer program device 306, generates a Fast FourierTransform at 505, and signals at 506 to the memory resource 304 that itwill store the generated outcome 505. Upon successful completion of theFFT storage, the memory resource 304 signals the non-transitory computerprogram device 306, to begin the retrieval step 507, which will triggerthe non-transitory computer program device 306, to begin computing theInverse Fast Fourier Transform at 508. This transformation is atranslation from the configuration space to frequency space and this isvery important in terms of exploring both transformations of certainproblems for more efficient computation and in exploring the powerspectrum of a signal. Once the Inverse Fast Fourier Transform (IFFT) hasbeen computed, the the non-transitory computer program device 306,begins the process of storing the IFFT to the memory resource 304 forpost processing during the decomposition and ultimately generating acommon-spread gathers.

As used herein the term “survey region” refers to an area or volume ofgeologic interest, and may be associated with the geometry, attitude andarrangement of the area or volume at any measurement scale. A region mayhave characteristics such as folding, faulting, cooling, unloading,and/or fracturing that has occurred therein.

As used herein, the term “computing” encompasses a wide variety ofactions, including calculating, determining, processing, deriving,investigation, look ups (e.g. looking up in a table, a database oranother data structure), ascertaining and the like. It may also includereceiving (e.g. receiving information), accessing (e.g. accessing datain a memory) and the like. Also, “computing” may include resolving,selecting, choosing, establishing, and the like.

As used herein, “subsurface”, and “subterranean” means beneath the topsurface of any mass of land at any elevation or over a range ofelevations, whether above, below or at sea level, and/or beneath thefloor surface of any mass of water, whether above, below or at sealevel.

Unless specifically stated otherwise, terms such as “defining”,“creating”, “including”, “representing”, “pre-analyzing”,“pre-defining”, “choosing”, “building”, “assigning”, “creating”,“introducing”, “eliminating”, “re-meshing”, “integrating”,“discovering”, “performing”, “predicting”, “determining”, “inputting”,“outputting”, “identifying”, “analyzing”, “using”, “assigning”,“disturbing”, “increasing”, “adjusting”, “incorporating”, “simulating”,“decreasing”, “distributing”, “specifying”, “extracting”, “displaying”,“executing”, “implementing”, and “managing”, or the like, may refer tothe action and processes of a retrieving system, or other electronicdevice, that transforms data represented as physical (electronic,magnetic, or optical) quantities within some electrical device'sstorage, like memory resources, or non-transitory computer readablememory, into other data similarly represented as physical quantitieswithin the storage, or in transmission or display devices.

Embodiments disclosed herein also relate to computer-implemented system,used as part of the retrieving system for performing the operationsherein. This system may be specially constructed for the requiredpurposes, or it may comprise a general-purpose computer selectivelyactivated or reconfigured by a computer program or code stored in thememory resources, or non-transitory computer readable memory. As such,the computer program or code may be stored or encoded in a computerreadable medium or implemented over some type of transmission medium. Acomputer-readable medium includes any medium or mechanism for storing ortransmitting information in a form readable by a machine, such as acomputer (‘machine’ and ‘computer’ may be used synonymously herein). Asa non-limiting example, a computer-readable medium may include acomputer-readable storage medium (e.g., read only memory (“ROM”), randomaccess memory (“RAM”), magnetic disk storage media, optical storagemedia, flash memory devices, etc.). A transmission medium may be twistedwire pairs, coaxial cable, optical fiber, or some other suitable wiredor wireless transmission medium, for transmitting signals such aselectrical, optical, acoustical or other form of propagated signals(e.g., carrier waves, infrared signals, digital signals, etc.)).

A receiving system or sensor 105 as used herein, typically includes atleast hardware capable of executing machine readable instructions, aswell as the software for executing acts (typically machine-readableinstructions) that produce a desired result. In addition, a retrievingsystem may include hybrids of hardware and software, as well as computersub-systems.

Hardware generally includes at least processor-capable platforms, suchas client-machines (also known as servers), and hand-held processingdevices (for example smart phones, personal digital assistants (PDAs),or personal computing devices (PCDs)).

Further, hardware may include any physical device that can storemachine-readable instructions, such as memory or other data storagedevices. Other forms of hardware include hardware sub-systems, includingtransfer devices such as modems, modem cards, ports, and port cards, forexample.

Software includes any machine code stored in any memory medium, such asRAM or ROM, and machine code stored on other devices (such asnon-transitory computer readable media like external hard drives, orflash memory, for example). Software may include source or object code,encompassing any set of instructions capable of being executed in aclient machine, server machine, remote desktop, or terminal.

Combinations of software and hardware could also be used for providingenhanced functionality and performance for certain embodiments of thedisclosed invention. One example is to directly manufacture softwarefunctions into a silicon chip. Accordingly, it should be understood thatcombinations of hardware and software are also included within thedefinition of a retrieving system and are thus envisioned by theinvention as possible equivalent structures and equivalent methods.

Computer-readable mediums or memory resources include passive datastorage, such as a random-access memory (RAM) as well as semi-permanentdata storage such as external hard drives, and external databases, forexample. In addition, an embodiment of the invention may be embodied inthe RAM of a computer to transform a standard computer into a newspecific computing machine.

Data structures are defined organizations of data that may enable anembodiment of the invention. For example, a data structure may providean organization of data, or an organization of executable code. Datasignals could be carried across non-transitory transmission mediums andstored and transported across various data structures, and, thus, may beused to transport an embodiment of the invention.

The system computer may be designed to work on any specificarchitecture. For example, the system may be executed on ahigh-performance computing system, which typically comprise theaggregation of multiple single computers, physically connected, orconnected over local area networks, client-server networks, wide areanetworks, internets, hand-held and other portable and wireless devicesand networks.

An “output device” includes the direct act that causes generating, aswell as any indirect act that facilitates generation. Indirect actsinclude providing software to an user, maintaining a website throughwhich a user is enabled to affect a display, hyperlinking to such awebsite, or cooperating or partnering with an entity who performs suchdirect or indirect acts. Thus, a user may operate alone or incooperation with a third-party vendor to enable the reference signal tobe generated on a display device. A display device may be included as anoutput device, and shall be suitable for displaying the requiredinformation, such as without limitation a CRT monitor, a LCD monitor, aplasma device, a flat panel device, or printer. The display device mayinclude a device which has been calibrated through the use of anyconventional software intended to be used in evaluating, correcting,and/or improving display results (e.g., a color monitor that has beenadjusted using monitor calibration software). Rather than (or inaddition to) displaying the reference image on a display device, amethod, consistent with the invention, may include providing a referenceimage to a subject. “Providing a reference image” may include creatingor distributing the reference image to the subject by physical,telephonic, or electronic delivery, providing access over a network tothe reference, or creating or distributing software to the subjectconfigured to run on the subject's workstation or computer including thereference image. In one example, providing of the reference image couldinvolve enabling the subject to obtain the reference image in hard copyform via a printer. For example, information, software, and/orinstructions could be transmitted (e.g., electronically or physicallyvia a data storage device or hard copy) and/or otherwise made available(e.g., via a network) in order to facilitate the subject using a printerto print a hard copy form of reference image. In such an example, theprinter may be a printer which has been calibrated through the use ofany conventional software intended to be used in evaluating, correcting,and/or improving printing results (e.g., a color printer that has beenadjusted using color correction software).

A database, or multiple databases may comprise any standard orproprietary database software, such as Oracle, Microsoft Access, SyBase,or DBase II, for example. The database may have fields, records, data,and other database elements that may be associated through databasespecific software. Additionally, data may be mapped. Mapping is theprocess of associating one data entry with another data entry. Forexample, the data contained in the location of a character file can bemapped to a field in a second table. The physical location of thedatabase is not limiting, and the database may be distributed. Forexample, the database may exist remotely from the server, and run on aseparate platform. Further, the database may be accessible across the alocal network, a wireless network of the Internet.

Furthermore, modules, features, attributes, methodologies, and otheraspects can be implemented as software, hardware, firmware or anycombination thereof. Wherever a component of the invention isimplemented as software, the component can be implemented as astandalone program, as part of a larger program, as a plurality ofseparate programs, as a statically or dynamically linked library, as akernel loadable module, as a device driver, and/or in every and anyother way known now or in the future to those of skill in the art ofcomputer programming. Additionally, the invention is not limited toimplementation in any specific operating system or environment.

Various terms as used herein are defined below. To the extent a termused in a claim is not defined below, it should be given the broadestpossible definition persons in the pertinent art have given that term asreflected in at least one printed publication or issued patent.

As used herein, “and/or” placed between a first entity and a secondentity means one of (1) the first entity, (2) the second entity, and (3)the first entity and the second entity. Multiple elements listed with“and/or” should be construed in the same fashion, i.e., “one or more” ofthe elements so conjoined

Additionally, the flowcharts and block diagrams in the Figuresillustrate the architecture, functionality, and operation of possibleimplementations of systems, methods, and computer program productsaccording to various embodiments of the present disclosure. It shouldalso be noted that, in some alternative implementations, the functionsnoted in the block may occur out of the order noted in the Figures. Forexamples, two blocks shown in succession may, in fact, be executedsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartsillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified hardware functions oracts, or combinations of special purpose hardware and computerinstructions.

While in the foregoing specification this disclosure has been describedin relation to certain preferred embodiments thereof, and many detailshave been set forth for purpose of illustration, the invention is not tobe unduly limited to the foregoing which has been set forth forillustrative purposes. On the contrary, a wide variety of modificationsand alternative embodiments will be apparent to a person skilled in theart, without departing from the true scope of the invention, as definedin the claims set forth below. Additionally, it should be appreciatedthat structural features or method steps shown or described in any oneembodiment herein can be used in other embodiments as well.

Symbols Table Symbol Brief Definition φ A curl-free scalar potential ϰArbitrary point location t Time ω Harmonic waves of frequency S Eikonalα P-wave velocity T Travel Time v and v′ Velocity x Location D_(X) _(c)(X, p′, ω) Decomposed Tau-P data from common spread data X Common spreadbeam center p′ Slowness r′ Trace location D_(X) _(c) (r′, ω) Recordedwavefield X_(c) Common Spread C₀ Constant value l_(X) _(c) (r) Commonspread X_(c) migration image at r location.

I claim:
 1. A computer-implemented method that employs compress-sensingmodels to efficiently migrate seismic over land-cross spreads, to revealan accurate location and amplitude of a target subsurfacehydro-carbonate using prestack seismic data acquired in a survey region.The method comprising: acquiring seismic model input data and landacquisition input data from a survey region; storing the acquiredseismic model input data and land acquisition input data to a memoryresource; retrieving the stored seismic model input data and landacquisition input data from the memory resource; regularizing theretrieved land acquisition input data by employing either flat surfaceor floating horizon algorithms; decomposing the retrieved seismic modelinput data into sparse common-spread beam centers; generatingcommon-spread gathers from the decomposed common-spread beam centers;filtering space-time signals from the generated common-spread gatherssharing regularized land acquisition input data; generating filteredcommon-spread gathers from the filtered space-time signals; storing thegenerated common-spread gathers, and the generated filteredcommon-spread gathers to a memory resource; retrieving the storedcommon-spread gathers from the memory resource; computing laser-beamraytracing, for each retrieved common-spread gather; generatinglaser-beam traced rays from the computed laser-beam raytracing; storingthe generated laser-beam traced rays to a memory resource; retrievingthe stored common-spread gathers, and laser-beam traced rays from thememory resource; computing common spread slant stacking for eachretrieved common-spread gathers, and laser-beam traced ray; generating acommon spread beam migration image from the computed common spread slantstacking; and storing the generated common spread beam migration imageto a memory resource.
 2. The method of claim 1, wherein the acquiredseismic model input data further comprises common cross-spread tracerecordings of wavefields, seismic amplitudes, and seismic travel-times;3. The method of claim 1, wherein the acquired land acquisition inputdata further comprises of common vector and azimuth offset gathers,common shot gathers, common receiver gather, shot line coordinates,receiver line coordinates, source slowness, and receiver slowness; 4.The method of claim 1, wherein decomposing the retrieved seismic modelinput data into sparse common-spread beams, further comprises generatingirregular beam centers or regular beam centers;
 5. The method of claim1, wherein computing laser-beam raytracing further comprises theexpression:i _(X) _(c) (r)=−C ₀Σ_(x) ∫dω∫∫dp _(x) ^(s) dp _(y) ^(r) U _(x)(r;X,p;ω)6. A computing system for performing a computer-implemented method thatemploys compress-sensing models to efficiently migrate seismic overland-cross spreads, to reveal an accurate location and amplitude of atarget subsurface hydro-carbonate using prestack seismic data acquiredin a survey region, comprising: a telemetry system for sending andreceiving seismic model input data and land acquisition input data froma survey region; a memory resource, for storing data corresponding tothe operations of computing, and generating; a computer system outputdevice; and a non-transitory computer readable memory device coupled tothe telemetry system, coupled to the memory resource, and coupled to thecomputer system output device, programmed for performing the operationsof: acquiring seismic model input data and land acquisition input datafrom a survey region; storing the acquired seismic model input data andland acquisition input data to a memory resource; retrieving the storedseismic model input data and land acquisition input data from the memoryresource; regularizing the retrieved land acquisition input data byemploying either flat surface or floating horizon algorithms;decomposing the retrieved seismic model input data into sparsecommon-spread beam centers; generating common-spread gathers from thedecomposed common-spread beam centers; filtering space-time signals fromthe generated common-spread gathers sharing regularized land acquisitioninput data; generating filtered common-spread gathers from the filteredspace-time signals; storing the generated common-spread gathers, and thegenerated filtered common-spread gathers to a memory resource;retrieving the stored common-spread gathers from the memory resource;computing laser-beam raytracing, for each retrieved common-spreadgather; generating laser-beam traced rays from the computed laser-beamraytracing; storing the generated laser-beam traced rays to a memoryresource; retrieving the stored common-spread gathers, and laser-beamtraced rays from the memory resource; computing common spread slantstacking for each retrieved common-spread gather, and laser-beam tracedray; generating a common spread beam migration image from the computedcommon spread slant stacking; and storing the generated common spreadbeam migration image to a memory resource.
 7. The computing system ofclaim 6, wherein decomposing the retrieved seismic model input data intosparse common-spread beam centers generates irregular or regularcommon-spread beams;
 8. The computing system of claim 6, wherein thenon-transitory computer readable memory device is further programmed toperform the operations of decomposing the retrieved seismic model inputdata into sparse common-spread beams for irregularly generatedcommon-spread beam centers, further comprising the steps of: retrievingthe land acquisition input data from the memory resource; acquiringlocal data gathers from the retrieved land acquisition input data;filtering the acquired local data gathers; clipping the filtered gatherslocal data gathers; shaping wavelets from clipped gathers to form beams;forming beams from the shaped wavelets; computing semblance on formedbeams; selecting an event and a wavelet for each computed semblance; andstoring selected events and wavelet.
 9. The computing system of claim 6,wherein the non-transitory computer readable memory device is furtherprogrammed to perform the operations of decomposing the retrievedseismic model input data into sparse common-spread beams for irregulargenerated common-spread beam centers, further comprising the expression:${{D_{X_{c}}\left( {X,p^{\prime},\omega} \right)} = {{\frac{\omega}{\omega_{x}}}{\int{\int{\frac{{dx}^{\prime}{dy}^{\prime}}{4\pi^{2}}{D_{X_{c}}\left( {r^{\prime},\omega} \right)}e^{\lbrack{{i\;\omega\;{p^{\prime} \cdot {({r^{\prime} - X})}}} - {{\frac{\omega}{\omega_{x}}}\frac{{{r^{\prime} - X}}^{2}}{2\omega_{x}^{2}}}}\rbrack}}}}}};$10. The computing system of claim 6, wherein the non-transitory computerreadable memory device is further programmed to perform the operationsof decomposing the retrieved seismic model input data into sparsecommon-spread beams for regular generated common-spread beam centers,further comprising the steps of: retrieving the land acquisition inputdata from the memory resource; acquiring local data gathers from theretrieved land acquisition input data; computing a Fast FourierTransformation on acquired local data gathers; generating Fast FourierTransformed regular common-spread beam centers from the computed FastFourier Transformation; storing the generated Fast Fourier Transformedregular common-spread beam centers; retrieving the stored Fast FourierTransformed regular beam centers; computing an Inverse Fast FourierTransformation on the retrieved Fast Fourier Transformed regular beamcenters; and store computed Inverse Fast Fourier Transformation regularbeam centers.